Non-holonomic vehicle motion has been studied extensively using physics-based models. Common approaches when using these models interpret the wheel/ground interactions using a linear tire model and thus may not fully capture the nonlinear and complex dynamics under various environments. On the other hand, neural network models have been widely employed in this domain, demonstrating powerful function approximation capabilities. However, these black-box learning strategies completely abandon the existing knowledge of well-known physics. In this paper, we seamlessly combine deep learning with a fully differentiable physics model to endow the neural network with available prior knowledge. The proposed model shows better generalization performance than the vanilla neural network model by a large margin. We also show that the latent features of our model can accurately represent lateral tire forces without the need for any additional training. Lastly, We develop a risk-aware model predictive controller using proprioceptive information derived from the latent features. We validate our idea in two autonomous driving tasks under unknown friction, outperforming the baseline control framework.
翻译:使用物理模型对非光学汽车运动进行了广泛研究。在使用这些模型时,使用线性轮胎模型对轮式/地面相互作用进行解释,因此可能无法充分捕捉各种环境中的非线性和复杂动态。另一方面,神经网络模型被广泛应用于这个领域,展示了强大的功能近似能力。然而,这些黑盒学习战略完全放弃了已知物理学的现有知识。在本文件中,我们将深层次学习与完全不同的物理模型完美地结合起来,使神经网络具有完全不同的先前知识。拟议的模型显示比香草神经网络模型的广效性效果要好得多。我们还表明,我们模型的潜在特征可以准确地代表横向轮胎力量,而不需要任何额外的培训。最后,我们利用从潜性特征中获得的自觉信息开发了一个风险觉模型预测控制器。我们用两种自主驱动任务来验证我们的想法,在未知摩擦下,比基线控制框架要好。